Traditional defect detection and classification are often artificial visual inspections. Quality inspection workers need to use related magnifying instruments to detect product surface defects. This has problems such as high labor costs, low efficiency, and low accuracy. Automated inspection methods based on machine vision can alleviate the above problems, so they have gradually become a trend to replace artificial visual inspection. Early defect detection methods based on machine vision used more artificially guided low-level visual features combined with traditional machine learning methods, by extracting global features of the image or by SIFT[1]}, HOG[2]}, SURF[3]}, LBP[4]} and other methods extract the local features of the image, then use neural network[5]}, SVM[6]} and other machine learning methods to achieve defect detection. Although traditional methods have achieved good results in defect detection and classification problems in the industrial field, this method needs to be based on rich professional knowledge and artificial fine-tuning. Deep learning abandons this tedious and inefficient artificial design. The feature step is to learn the inherent feature distribution of data based on a data-driven approach by designing a multilayer neural network. This deep model obtained through learning has achieved relatively advanced performance in image analysis and recognition. Wei et al. proposed a four-stage defect detection framework[7]}, and formulating the defect detection problem into pixel segmentation and defect classification based on regression, which not only can provides pixel-level defect severity and defect types, but also makes users more flexible in use. Song L et al. improved the U-Net framework and proposed DU-Net[8]} based on the Deep Convolutional Neural Network (DCNN), using two white light sources as the lighting system, and deconvolution instead of upsampling so that the model can extract more features. At the same time, the number of feature channels is doubled in the down-sampling step, so that the network input and output sizes are the same to avoid the edge loss of the image. The author proves the effectiveness of this method through experiments.
